Current bytecode instrumentation based performance monitoring tools provide performance data at a fine grained level, down to data describing single method calls. Information at this detailed level is a great improvement to find and eliminate performance problems more quickly and efficiently.
The acquisition of such high-quality information requires the placement of a large quantity of bytecode instrumentations for performance monitoring which are injected into the bytecode of the monitored application. This multitude of monitoring sensors also creates a large amount of measurement data which has to be aggregated and analyzed.
Current byte code instrumentation based performance monitoring tools perform instrumentation of application bytecode inside the monitored application and also aggregate measurement data within the memory of the monitored application. Although this approach eases interpretation of the measurement data because correlation between acquired measurement data and the sensor that created the data and its context can be easily performed, it creates considerable overhead in terms of processing time and memory consumption.
Especially capturing of SQL statements and monitoring the execution of said SQL statements creates significant memory overhead, because SQL statements typically consist of long strings and capturing those strings requires a considerable amount of memory. In the worst case, excessive capturing of SQL statements may cause an out of memory error which may lead to a crash of the monitored application.
Consequently, an alternative approach is required which transfers measurement data out of the monitored application after acquisition to keep memory overhead low and to guarantee that a maximum memory overhead is not exceeded at any time. Additionally, the new approach should allow performing bytecode instrumentation outside of the application to reduce the processing overhead caused by bytecode instrumentation.
Current approaches which transfer measurement and logging data from a monitored application to a centralized server which aggregates and correlates the measurement data, like distributed logging systems or the monitoring framework of TPTP, a plug-in to the well known eclipse development environment, generate significant overhead in terms of network traffic. The reason for this overhead is redundant context data sent together with measurement data because those approaches retransfer context information required to interpret that measurement data with every captured measurement.
An alternative approach should also reduce the network overhead generated by transferring the measurement data, by storing context information required to interpret captured measurement data, and reuse this stored context information whenever this is possible instead of retransferring it every time measurement data is sent.